In the systems arena two design problems prominently reign. One has as its fundamental goal the efficient storage of signals that are produced by a signal source of either artificial or biological origin, e.g., voice, music, video and computer data sources. The other relates to the efficient processing of these signals that may for instance result in their Fourier transform, covariance, etc. The design of efficient signal storage algorithms relies heavily on source coding. The area of source coding has a conspicuous recent history and has been one of the enabling technologies for what is known today as the information revolution. The reason why this is the case is because source coding provides a sound practical and theoretical measure for the information associated with any signal source output event and its average value or entropy. This information can then be used to provide an efficient replacement or source coder for the signal source that can be either lossless or lossy depending if its output matches that of the signal source. Examples of lossless source coders are Huffman, Entropy, and Arithmetic coders as described in The Communications Handbook, J. D. Gibson, ed., IEEE Press, 1997. For the lossy case the standards of JPEG, MPEG and wavelets based JPEG2000, predictive-transform (PT) source-coding, etc., have been advanced. See Predictive-Transform Source Coding with Bit Planes, Feria and Licul, Submitted to 2006 IEEE Conference on Systems, Man and Cybernetics, October 2006.
The design of efficient signal processing techniques is approached with a myriad of techniques that, unfortunately, are not similarly guided by a theoretical framework that encompasses both lossless and lossy solutions.
A real-world problem whose high performance is attributed to its use of an intelligent system (IS) is knowledge-aided (KA) airborne moving target indicator (AMTI) radar such as found in DARPA's knowledge aided sensory signal processing expert reasoning (KASSPER). The IS includes two subsystems in cascade. The first subsystem is a memory device containing the intelligence or prior knowledge. The intelligence is clutter whose knowledge facilitates the detection of a moving target. The clutter is available in the form of synthetic aperture radar (SAR) imagery where each SAR image requires 4 MB of memory space. Since the required memory space for SAR imagery is prohibitive, it then becomes necessary to use ‘lossy’ memory space compression source coding schemes to address this problem of memory space.
The second subsystem of the IS architecture is the intelligence processor (IP) which is a clutter covariance processor (CCP). The CCP is characterized by the on-line computation of a large number of complex matrices where a typical dimension for these matrices is 256×256 which results when both the number of antenna elements and transmitted antenna pulses during a coherent pulse interval (CPI) is 16. Clearly these computations significantly slow down the on-line derivation of the pre-requisite clutter covariances.
The present invention addresses these CCP computational issues using a novel time compression processor coding methodology that inherently arises as the ‘time compression dual’ of space compression source coding. Further, missing from the art is a lossy signal processor that utilizes efficient signal processing techniques to achieve high speed results having a high confidence level of accuracy. The present invention can satisfy one or more of these and other needs.